{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"-1\">\n",
    "Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits. Leverage can work against you. Due to the certain restrictions imposed by the local law and regulation, German resident retail client(s) could sustain a total loss of deposited funds but are not subject to subsequent payment obligations beyond the deposited funds. Be aware and fully understand all risks associated with the market and trading. Prior to trading any products, carefully consider your financial situation and experience level. Any opinions, news, research, analyses, prices, or other information is provided as general market commentary, and does not constitute investment advice. FXCM & TPQ will not accept liability for any loss or damage, including without limitation to, any loss of profit, which may arise directly or indirectly from use of or reliance on such information.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The author is neither an employee, agent nor representative of FXCM and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of FXCM or any form of personal or investment advice. FXCM assumes no responsibility for any loss or damage, including but not limited to, any loss or gain arising out of the direct or indirect use of this or any other content. Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Tick Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pylab import mpl, plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fxcmpy import fxcmpy_tick_data_reader as tdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"
     ]
    }
   ],
   "source": [
    "print(tdr.get_available_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2018, 6, 25)  \n",
    "stop = dt.datetime(2018, 6, 30)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "td = tdr('EURUSD', start, stop)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1963779 entries, 06/24/2018 21:00:12.290 to 06/29/2018 20:59:00.607\n",
      "Data columns (total 2 columns):\n",
      "Bid    float64\n",
      "Ask    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 44.9+ MB\n"
     ]
    }
   ],
   "source": [
    "td.get_raw_data().info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1963779 entries, 2018-06-24 21:00:12.290000 to 2018-06-29 20:59:00.607000\n",
      "Data columns (total 2 columns):\n",
      "Bid    float64\n",
      "Ask    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 44.9 MB\n"
     ]
    }
   ],
   "source": [
    "td.get_data().info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:12.290</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:16.046</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:22.846</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:22.907</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 21:00:23.441</th>\n",
       "      <td>1.1662</td>\n",
       "      <td>1.16663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Bid      Ask\n",
       "2018-06-24 21:00:12.290  1.1662  1.16660\n",
       "2018-06-24 21:00:16.046  1.1662  1.16650\n",
       "2018-06-24 21:00:22.846  1.1662  1.16658\n",
       "2018-06-24 21:00:22.907  1.1662  1.16660\n",
       "2018-06-24 21:00:23.441  1.1662  1.16663"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td.get_data().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = td.get_data(start='2018-06-29 12:00:00',\n",
    "                  end='2018-06-29 12:15:00')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bid</th>\n",
       "      <th>Ask</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.011</th>\n",
       "      <td>1.16497</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.071</th>\n",
       "      <td>1.16497</td>\n",
       "      <td>1.16497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.079</th>\n",
       "      <td>1.16497</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.091</th>\n",
       "      <td>1.16495</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 12:00:00.205</th>\n",
       "      <td>1.16496</td>\n",
       "      <td>1.16498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Bid      Ask\n",
       "2018-06-29 12:00:00.011  1.16497  1.16498\n",
       "2018-06-29 12:00:00.071  1.16497  1.16497\n",
       "2018-06-29 12:00:00.079  1.16497  1.16498\n",
       "2018-06-29 12:00:00.091  1.16495  1.16498\n",
       "2018-06-29 12:00:00.205  1.16496  1.16498"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Mid'] = sub.mean(axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['SMA'] = sub['Mid'].rolling(1000).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub[['Mid', 'SMA']].plot(figsize=(10, 6), lw=0.75);\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_01.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Candles Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fxcmpy import fxcmpy_candles_data_reader as cdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"
     ]
    }
   ],
   "source": [
    "print(cdr.get_available_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2018, 5, 1)\n",
    "stop = dt.datetime(2018, 6, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`period` must be one of `m1`, `H1` or `D1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "period = 'H1'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = cdr('EURUSD', start, stop, period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = candles.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1080 entries, 2018-04-29 21:00:00 to 2018-06-29 20:00:00\n",
      "Data columns (total 8 columns):\n",
      "BidOpen     1080 non-null float64\n",
      "BidHigh     1080 non-null float64\n",
      "BidLow      1080 non-null float64\n",
      "BidClose    1080 non-null float64\n",
      "AskOpen     1080 non-null float64\n",
      "AskHigh     1080 non-null float64\n",
      "AskLow      1080 non-null float64\n",
      "AskClose    1080 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 75.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BidOpen</th>\n",
       "      <th>BidHigh</th>\n",
       "      <th>BidLow</th>\n",
       "      <th>BidClose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-29 16:00:00</th>\n",
       "      <td>1.16768</td>\n",
       "      <td>1.16820</td>\n",
       "      <td>1.16731</td>\n",
       "      <td>1.16769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 17:00:00</th>\n",
       "      <td>1.16769</td>\n",
       "      <td>1.16826</td>\n",
       "      <td>1.16709</td>\n",
       "      <td>1.16781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 18:00:00</th>\n",
       "      <td>1.16781</td>\n",
       "      <td>1.16816</td>\n",
       "      <td>1.16668</td>\n",
       "      <td>1.16684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 19:00:00</th>\n",
       "      <td>1.16684</td>\n",
       "      <td>1.16792</td>\n",
       "      <td>1.16638</td>\n",
       "      <td>1.16774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 20:00:00</th>\n",
       "      <td>1.16774</td>\n",
       "      <td>1.16904</td>\n",
       "      <td>1.16758</td>\n",
       "      <td>1.16816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BidOpen  BidHigh   BidLow  BidClose\n",
       "2018-06-29 16:00:00  1.16768  1.16820  1.16731   1.16769\n",
       "2018-06-29 17:00:00  1.16769  1.16826  1.16709   1.16781\n",
       "2018-06-29 18:00:00  1.16781  1.16816  1.16668   1.16684\n",
       "2018-06-29 19:00:00  1.16684  1.16792  1.16638   1.16774\n",
       "2018-06-29 20:00:00  1.16774  1.16904  1.16758   1.16816"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.columns[:4]].tail()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AskOpen</th>\n",
       "      <th>AskHigh</th>\n",
       "      <th>AskLow</th>\n",
       "      <th>AskClose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-29 16:00:00</th>\n",
       "      <td>1.16769</td>\n",
       "      <td>1.16820</td>\n",
       "      <td>1.16732</td>\n",
       "      <td>1.16771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 17:00:00</th>\n",
       "      <td>1.16771</td>\n",
       "      <td>1.16827</td>\n",
       "      <td>1.16711</td>\n",
       "      <td>1.16782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 18:00:00</th>\n",
       "      <td>1.16782</td>\n",
       "      <td>1.16817</td>\n",
       "      <td>1.16669</td>\n",
       "      <td>1.16686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 19:00:00</th>\n",
       "      <td>1.16686</td>\n",
       "      <td>1.16794</td>\n",
       "      <td>1.16640</td>\n",
       "      <td>1.16775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 20:00:00</th>\n",
       "      <td>1.16775</td>\n",
       "      <td>1.16907</td>\n",
       "      <td>1.16760</td>\n",
       "      <td>1.16861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AskOpen  AskHigh   AskLow  AskClose\n",
       "2018-06-29 16:00:00  1.16769  1.16820  1.16732   1.16771\n",
       "2018-06-29 17:00:00  1.16771  1.16827  1.16711   1.16782\n",
       "2018-06-29 18:00:00  1.16782  1.16817  1.16669   1.16686\n",
       "2018-06-29 19:00:00  1.16686  1.16794  1.16640   1.16775\n",
       "2018-06-29 20:00:00  1.16775  1.16907  1.16760   1.16861"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.columns[4:]].tail()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['MidClose'] = data[['BidClose', 'AskClose']].mean(axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SMA1'] = data['MidClose'].rolling(30).mean()  \n",
    "data['SMA2'] = data['MidClose'].rolling(100).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['MidClose', 'SMA1', 'SMA2']].plot(figsize=(10, 6));\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_02.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fxcmpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.1.34'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fxcmpy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api = fxcmpy.fxcmpy(config_file='../../cfg/fxcm.cfg')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruments = api.get_instruments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF', 'EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD', 'EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'CHF/JPY', 'GBP/CHF', 'EUR/AUD', 'EUR/CAD', 'AUD/CAD', 'AUD/JPY', 'CAD/JPY', 'NZD/JPY', 'GBP/CAD', 'GBP/NZD', 'GBP/AUD', 'AUD/NZD', 'USD/SEK', 'EUR/SEK', 'EUR/NOK', 'USD/NOK', 'USD/MXN', 'AUD/CHF', 'EUR/NZD', 'USD/ZAR', 'USD/HKD', 'ZAR/JPY', 'USD/TRY', 'EUR/TRY', 'NZD/CHF', 'CAD/CHF', 'NZD/CAD', 'TRY/JPY', 'USD/CNH', 'AUS200', 'ESP35', 'FRA40', 'GER30', 'HKG33', 'JPN225', 'NAS100', 'SPX500', 'UK100', 'US30', 'Copper', 'CHN50', 'EUSTX50', 'USDOLLAR', 'USOil', 'UKOil', 'SOYF', 'NGAS', 'Bund', 'XAU/USD', 'XAG/USD']\n"
     ]
    }
   ],
   "source": [
    "print(instruments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Historical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('USD/JPY', period='D1', number=10)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-12 21:00:00</th>\n",
       "      <td>112.014</td>\n",
       "      <td>112.534</td>\n",
       "      <td>112.627</td>\n",
       "      <td>111.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-13 21:00:00</th>\n",
       "      <td>112.534</td>\n",
       "      <td>112.330</td>\n",
       "      <td>112.801</td>\n",
       "      <td>112.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-15 21:00:00</th>\n",
       "      <td>112.330</td>\n",
       "      <td>112.200</td>\n",
       "      <td>112.364</td>\n",
       "      <td>112.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-16 21:00:00</th>\n",
       "      <td>112.200</td>\n",
       "      <td>112.272</td>\n",
       "      <td>112.562</td>\n",
       "      <td>112.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-17 21:00:00</th>\n",
       "      <td>112.272</td>\n",
       "      <td>112.871</td>\n",
       "      <td>112.925</td>\n",
       "      <td>112.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18 21:00:00</th>\n",
       "      <td>112.871</td>\n",
       "      <td>112.835</td>\n",
       "      <td>113.138</td>\n",
       "      <td>112.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19 21:00:00</th>\n",
       "      <td>112.835</td>\n",
       "      <td>112.462</td>\n",
       "      <td>113.176</td>\n",
       "      <td>112.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20 21:00:00</th>\n",
       "      <td>112.462</td>\n",
       "      <td>111.422</td>\n",
       "      <td>112.623</td>\n",
       "      <td>111.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-22 21:00:00</th>\n",
       "      <td>111.360</td>\n",
       "      <td>111.380</td>\n",
       "      <td>111.392</td>\n",
       "      <td>111.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23 21:00:00</th>\n",
       "      <td>111.380</td>\n",
       "      <td>111.338</td>\n",
       "      <td>111.544</td>\n",
       "      <td>110.752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow\n",
       "date                                                    \n",
       "2018-07-12 21:00:00  112.014   112.534  112.627  111.915\n",
       "2018-07-13 21:00:00  112.534   112.330  112.801  112.276\n",
       "2018-07-15 21:00:00  112.330   112.200  112.364  112.100\n",
       "2018-07-16 21:00:00  112.200   112.272  112.562  112.198\n",
       "2018-07-17 21:00:00  112.272   112.871  112.925  112.228\n",
       "2018-07-18 21:00:00  112.871   112.835  113.138  112.710\n",
       "2018-07-19 21:00:00  112.835   112.462  113.176  112.064\n",
       "2018-07-20 21:00:00  112.462   111.422  112.623  111.382\n",
       "2018-07-22 21:00:00  111.360   111.380  111.392  111.165\n",
       "2018-07-23 21:00:00  111.380   111.338  111.544  110.752"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[candles.columns[:4]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-12 21:00:00</th>\n",
       "      <td>112.028</td>\n",
       "      <td>112.547</td>\n",
       "      <td>112.628</td>\n",
       "      <td>111.919</td>\n",
       "      <td>302016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-13 21:00:00</th>\n",
       "      <td>112.547</td>\n",
       "      <td>112.343</td>\n",
       "      <td>112.802</td>\n",
       "      <td>112.278</td>\n",
       "      <td>252161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-15 21:00:00</th>\n",
       "      <td>112.343</td>\n",
       "      <td>112.242</td>\n",
       "      <td>112.399</td>\n",
       "      <td>112.118</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-16 21:00:00</th>\n",
       "      <td>112.242</td>\n",
       "      <td>112.302</td>\n",
       "      <td>112.565</td>\n",
       "      <td>112.217</td>\n",
       "      <td>200248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-17 21:00:00</th>\n",
       "      <td>112.302</td>\n",
       "      <td>112.892</td>\n",
       "      <td>112.926</td>\n",
       "      <td>112.228</td>\n",
       "      <td>286666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18 21:00:00</th>\n",
       "      <td>112.892</td>\n",
       "      <td>112.845</td>\n",
       "      <td>113.139</td>\n",
       "      <td>112.711</td>\n",
       "      <td>251874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19 21:00:00</th>\n",
       "      <td>112.845</td>\n",
       "      <td>112.477</td>\n",
       "      <td>113.179</td>\n",
       "      <td>112.065</td>\n",
       "      <td>313821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20 21:00:00</th>\n",
       "      <td>112.477</td>\n",
       "      <td>111.481</td>\n",
       "      <td>112.625</td>\n",
       "      <td>111.388</td>\n",
       "      <td>413345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-22 21:00:00</th>\n",
       "      <td>111.400</td>\n",
       "      <td>111.396</td>\n",
       "      <td>111.402</td>\n",
       "      <td>111.186</td>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23 21:00:00</th>\n",
       "      <td>111.396</td>\n",
       "      <td>111.350</td>\n",
       "      <td>111.546</td>\n",
       "      <td>110.754</td>\n",
       "      <td>307482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     askopen  askclose  askhigh   asklow  tickqty\n",
       "date                                                             \n",
       "2018-07-12 21:00:00  112.028   112.547  112.628  111.919   302016\n",
       "2018-07-13 21:00:00  112.547   112.343  112.802  112.278   252161\n",
       "2018-07-15 21:00:00  112.343   112.242  112.399  112.118      547\n",
       "2018-07-16 21:00:00  112.242   112.302  112.565  112.217   200248\n",
       "2018-07-17 21:00:00  112.302   112.892  112.926  112.228   286666\n",
       "2018-07-18 21:00:00  112.892   112.845  113.139  112.711   251874\n",
       "2018-07-19 21:00:00  112.845   112.477  113.179  112.065   313821\n",
       "2018-07-20 21:00:00  112.477   111.481  112.625  111.388   413345\n",
       "2018-07-22 21:00:00  111.400   111.396  111.402  111.186      563\n",
       "2018-07-23 21:00:00  111.396   111.350  111.546  110.754   307482"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[candles.columns[4:]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2017, 1, 1)  \n",
    "end = dt.datetime(2018, 1, 1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('EUR/GBP', period='D1',\n",
    "                          start=start, stop=end)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 309 entries, 2017-01-03 22:00:00 to 2018-01-01 22:00:00\n",
      "Data columns (total 9 columns):\n",
      "bidopen     309 non-null float64\n",
      "bidclose    309 non-null float64\n",
      "bidhigh     309 non-null float64\n",
      "bidlow      309 non-null float64\n",
      "askopen     309 non-null float64\n",
      "askclose    309 non-null float64\n",
      "askhigh     309 non-null float64\n",
      "asklow      309 non-null float64\n",
      "tickqty     309 non-null int64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 24.1 KB\n"
     ]
    }
   ],
   "source": [
    "candles.info()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter `period` must be one of `m1, m5, m15, m30, H1, H2, H3, H4, H6, H8, D1, W1` or `M1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('EUR/USD', period='m1', number=250)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candles['askclose'].plot(figsize=(10, 6))\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_03.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output(data, dataframe):\n",
    "    print('%3d | %s | %s | %6.5f, %6.5f' \n",
    "          % (len(dataframe), data['Symbol'],\n",
    "             pd.to_datetime(int(data['Updated']), unit='ms'), \n",
    "             data['Rates'][0], data['Rates'][1]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1 | EUR/USD | 2018-07-24 12:40:06.370000 | 1.16941, 1.16943\n",
      "  2 | EUR/USD | 2018-07-24 12:40:06.950000 | 1.16941, 1.16942\n",
      "  3 | EUR/USD | 2018-07-24 12:40:07.999000 | 1.16938, 1.16939\n",
      "  4 | EUR/USD | 2018-07-24 12:40:08.282000 | 1.16937, 1.16939\n",
      "  5 | EUR/USD | 2018-07-24 12:40:08.988000 | 1.16939, 1.16941\n",
      "  6 | EUR/USD | 2018-07-24 12:40:09.479000 | 1.16941, 1.16943\n"
     ]
    }
   ],
   "source": [
    "api.subscribe_market_data('EUR/USD', (output,))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bid     1.16941\n",
       "Ask     1.16943\n",
       "High    1.17169\n",
       "Low     1.16546\n",
       "Name: 2018-07-24 12:40:09.479000, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_last_price('EUR/USD')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  7 | EUR/USD | 2018-07-24 12:40:10.007000 | 1.16940, 1.16943\n"
     ]
    }
   ],
   "source": [
    "api.unsubscribe_market_data('EUR/USD')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placing Orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_open_positions()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/USD', 100)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = ['tradeId', 'amountK', 'currency',\n",
    "       'grossPL', 'isBuy']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61471172</td>\n",
       "      <td>100</td>\n",
       "      <td>EUR/USD</td>\n",
       "      <td>-2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tradeId  amountK currency  grossPL  isBuy\n",
       "0  61471172      100  EUR/USD       -2   True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_open_positions()[sel]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/GBP', 50)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61471172</td>\n",
       "      <td>100</td>\n",
       "      <td>EUR/USD</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61471174</td>\n",
       "      <td>50</td>\n",
       "      <td>EUR/GBP</td>\n",
       "      <td>-3.27961</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tradeId  amountK currency  grossPL  isBuy\n",
       "0  61471172      100  EUR/USD  1.00000   True\n",
       "1  61471174       50  EUR/GBP -3.27961   True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_open_positions()[sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_sell_order('EUR/USD', 25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/GBP', 50)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61471174</td>\n",
       "      <td>50</td>\n",
       "      <td>EUR/GBP</td>\n",
       "      <td>-3.93560</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61471213</td>\n",
       "      <td>75</td>\n",
       "      <td>EUR/USD</td>\n",
       "      <td>2.25000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61471177</td>\n",
       "      <td>50</td>\n",
       "      <td>EUR/GBP</td>\n",
       "      <td>-1.31187</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tradeId  amountK currency  grossPL  isBuy\n",
       "0  61471174       50  EUR/GBP -3.93560   True\n",
       "1  61471213       75  EUR/USD  2.25000   True\n",
       "2  61471177       50  EUR/GBP -1.31187   True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_open_positions()[sel]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.close_all_for_symbol('EUR/GBP')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "    tradeId  amountK currency  grossPL  isBuy\n",
       "0  61471213       75  EUR/USD        6   True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_open_positions()[sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.close_all()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "Empty DataFrame\n",
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     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_open_positions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Account Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "988162"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_default_account()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accountId</th>\n",
       "      <td>988162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accountName</th>\n",
       "      <td>1000988162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>42711.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayPL</th>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equity</th>\n",
       "      <td>42711.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grossPL</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hedging</th>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mc</th>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcDate</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratePrecision</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin</th>\n",
       "      <td>42711.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin3</th>\n",
       "      <td>42711.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin3Perc</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMarginPerc</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usdMr</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usdMr3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "accountId              988162\n",
       "accountName        1000988162\n",
       "balance               42711.8\n",
       "dayPL                    85.2\n",
       "equity                42711.8\n",
       "grossPL                     0\n",
       "hedging                     N\n",
       "mc                          N\n",
       "mcDate                       \n",
       "ratePrecision               0\n",
       "t                           6\n",
       "usableMargin          42711.8\n",
       "usableMargin3         42711.8\n",
       "usableMargin3Perc         100\n",
       "usableMarginPerc          100\n",
       "usdMr                       0\n",
       "usdMr3                      0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_accounts().T  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
 ],
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